Update app.py

app.py

@@ -93,10 +93,22 @@ def reduce_zonal_ndvi(image, ee_object):
   )
   return image.set(reduced)
 
+# Calculate NDVI
 def calculate_NDVI(image):
     ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
     return ndvi
 
+# Get Zonal NDVI for Year on Year Profile
+def get_zonal_ndviYoY(collection, ee_object):
+    ndvi_collection = collection.map(calculate_NDVI)
+    max_ndvi = ndvi_collection.max()
+    reduced_max_ndvi = max_ndvi.reduceRegion(
+        reducer=ee.Reducer.mean(),
+        geometry=ee_object.geometry(),
+        scale=10,
+        maxPixels=1e12)
+    return reduced_max_ndvi.get('NDVI').getInfo()
+
 # Get Zonal NDVI
 def get_zonal_ndvi(collection, geom_ee_object):
   reduced_collection = collection.map(lambda image: reduce_zonal_ndvi(image, ee_object=geom_ee_object))
@@ -106,16 +118,6 @@ def get_zonal_ndvi(collection, geom_ee_object):
   df = pd.DataFrame({'NDVI': stats_list, 'Date': dates, 'Imagery': filenames})
   return df
 
-def get_zonal_ndviYoY(collection, ee_object):
-    ndvi_collection = collection.map(calculate_NDVI)
-    max_ndvi = ndvi_collection.max()
-    reduced_max_ndvi = max_ndvi.reduceRegion(
-        reducer=ee.Reducer.mean(),
-        geometry=ee_object.geometry(),
-        scale=10,
-        maxPixels=1e12)
-    return reduced_max_ndvi
-    
 
 # put title in center
 st.markdown("""
@@ -203,27 +205,31 @@ if uploaded_file is not None:
         st.line_chart(resultant_df[['AvgNDVI_Inside', 'Avg_NDVI_Buffer', 'Date']].set_index('Date'))
 
         ####### YoY Profile ########
-        start_year = 2017
+        start_year = 2019
         end_year = 2023
 
         max_ndvi_geoms = []
         max_ndvi_buffered_geoms = []
         for year in range(start_year, end_year+1):
-            start_ddmm = start_date[:4] + str(year)
-            end_ddmm = end_date[:4] + str(year)
+            
+            # Construct start and end dates for every year
+            start_ddmm = str(year)+pd.to_datetime(start_date).strftime("-%m-%d")
+            end_ddmm = str(year)+pd.to_datetime(end_date).strftime("-%m-%d")     
             
             # Filter data based on the date, bounds, cloud coverage and select NIR and Red Band
             collection = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED").filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', max_cloud_cover)).filter(ee.Filter.date(start_ddmm, end_ddmm)).select(['B4', 'B8'])
-
+        
             # Get Zonal NDVI based on collection and geometries (Original KML and Buffered KML)
             max_ndvi_geoms.append(get_zonal_ndviYoY(collection.filterBounds(geom_ee_object), geom_ee_object))
             max_ndvi_buffered_geoms.append(get_zonal_ndviYoY(collection.filterBounds(buffered_ee_object), buffered_ee_object))
-
+        
+        # Create a DataFrame for YoY profile
         yoy_df = pd.DataFrame({'Year': list(range(start_year, end_year+1)), 'NDVI_Inside': max_ndvi_geoms, 'NDVI_Buffer': max_ndvi_buffered_geoms})
+        yoy_df['Ratio'] = yoy_df['NDVI_Inside'] / yoy_df['NDVI_Buffer']
 
         # plot the time series
         st.write("Year on Year Plot")
-        st.line_chart(yoy_df[['NDVI_Inside', 'NDVI_Buffer', 'Year']].set_index('Year'))
+        st.line_chart(yoy_df[['NDVI_Inside', 'NDVI_Buffer', 'Ratio', 'Year']].set_index('Year'))
         
         # Visualize map on ESRI basemap
         st.write("Map Visualization")